Adaptive Principal Component Analysis
Adaptive Principal Component Analysis
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait Analysis for Human Identification in Frequency Domain
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Layered Deformable Model for Gait Analysis
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
3D Gait Recognition Using Multiple Cameras
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Human gait recognition at sagittal plane
Image and Vision Computing
Outdoor recognition at a distance by fusing gait and face
Image and Vision Computing
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
What image information is important in silhouette-based gait recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Gait recognition based on fusion of multi-view gait sequences
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Gait recognition using a view transformation model in the frequency domain
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Gait Recognition Using Radon Transform and Linear Discriminant Analysis
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Human Gait Recognition With Matrix Representation
IEEE Transactions on Circuits and Systems for Video Technology
Reducing the effect of noise on human contour in gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Gas discharge visualization: an imaging and modeling tool for medical biometrics
Journal of Biomedical Imaging
Audio pacemaker: walking, talking indigenous knowledge
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
Feature subset selection applied to model-free gait recognition
Image and Vision Computing
Gait recognition using description of shape synthesized by planar homography
The Journal of Supercomputing
Gait recognition based on shape and motion analysis of silhouette contours
Computer Vision and Image Understanding
Hi-index | 0.10 |
The strength of gait, compared to other biometrics, is that it does not require cooperative subjects. In previous work gait recognition approaches were evaluated using a gallery set consisting of gait sequences of people under similar covariate conditions (e.g. clothing, surface, carrying, and view conditions). This evaluation procedure, however, implies that the gait data are collected in a cooperative manner so that the covariate conditions are known a priori. In this work, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches would drop drastically under this more realistic experimental setup. We argue that selecting the most relevant gait features that are invariant to changes in gait covariate conditions is the key to develop a gait recognition system that works without subject cooperation. To this end, Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis (ACDA) is formulated which seamlessly integrates our feature selection method with subspace analysis for robust recognition, and importantly is computationally much more efficient compared to the conventional Component and Discriminant Analysis. Experiments are carried out on two comprehensive benchmarking databases: the CASIA database and the Southampton Human ID at a distance gait database (SOTON database). Our results demonstrate that the proposed approach significantly outperforms the existing techniques particularly when gait is captured with variable and unknown covariate conditions.